We present a novel method for the localization of an autonomous vehicle in two dimensions. Instead of focussing on properties of obstacles, we emphasize the description of free-space. We introduce the concept of signature graphs which is a compact data structure describing available free-space of an environment. It therefore also represents a map of the environment. A signature is an edge in the graph and is defined by seven parameters. An edge indicates that it is possible for the vehicle to move between two graph nodes. The signature graph is flexible and can easily be updated as new information about the environment is collected. We have with an autonomous wheelchair implemented our methods and tests them with successful results

If a wheelchair for disabled is used for semi-autonomous navigation indoors it must be able to navigate through doors. A door and doorway can be parameterized with five parameters. A divide and conquer implementation of the Hough transform is used to segment outlines from range scans. The client software remote controls the wheelchair from the MATLAB environment. The software consists of several Java threads that run concurrently. Sensor data are polled by threads and put into databases to reduce the network lag. The databases are used by a controller and a Kalman filter. Since most of the implementation is coded in Java it is possible to run it as a stand alone program on a computer that has Java installed. From 10 runs the trajectory offset was calculated to 0.9 cm with a standard deviation of 1.4 cm. The standard deviation of the heading was 2.2 degrees. This performance is essentially independent of the initial starting pose.

The mechatronics course at Lulea University of Technology is divided into a traditional part with lectures, problem solving, lab exercises, and a project part. During 2002-2003, the project has been to design and build autonomous model race cars. A public race is arranged at the end of the course. The race track is an outdoor track. In year 2002, it was defined by a painted white line on asphalt. In 2003, it was defined by an electrical wire carrying 100 mA of current at 10 kHz frequency. The students had to design and build sensors, amplifiers, filters and interface circuits for the onboard PICI6F876 microcontroller as well as some power electronics to control the drive DC-motor. The race is obviously extremely challenging for the students and as a result, they spend much more time on the course than before.

This paper describes two related tests; to navigate an articulated lawn mower and to reverse a mobile robot with a trailer. In both cases the vehicles are to follow a prespecified trajectory. The navigation principal is based on measured directions to several identical beacons, consisting of strips of reflective tapes. The angular sensor is a rotating laser for the illumination of the beacons and a highly sensitive electro-optical receiver for detecting the directions to the beacons. A Kalman filter is used to combine the measurements from the odometers with the detected angles to the known position of the beacons. To measure the angle between the robot and the trailer the same laser was used. This was done by placing two reflective beacons on the trailer. The repeatability was within 2 centimetre at low speed. The navigation of these two different types of vehicles turns out to be, essentially, the same problem. The sensitivities are different. Emphasis is on robust state estimation.>